An Overloaded MU-MIMO Signal Detection Method Using Piecewise Continuous Nonconvex Sparse Regularizer
An Overloaded MU-MIMO Signal Detection Method Using Piecewise Continuous Nonconvex Sparse Regularizer
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发表时间：
2021-12
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通讯作者：
Atsuya Hirayama;K. Hayashi
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作者：
Atsuya Hirayama;K. Hayashi
在本文中，我们考虑了超载多用户多输入多输出（MU-MIMO）正交频施加（OFDM）和带有环状前缀（SC-CP）系统的单载波传输的信号检测问题。对于系统，我们采用具有群稀疏（IWSCSR-GS）优化的复杂稀疏正规化器的迭代加权总和，这是一种复杂的离散值矢量重建方法，使用符号的离散性来估计未知矢量，并提出一种使用信号进行构造方法的方法分段连续的非凸稀疏的正规化器，例如平滑剪切的绝对偏差（SCAD）或minimax凹额（MCP），在优化问题中。计算机仿真结果表明，具有MCP的提议的信号重建方法可实现更好的符号错误率（SER）性能，不仅是具有L1 NORM的IWSCSR-GS，还具有LP Norm的性能（P = 0,1/2,2/3） ）或L1-L2差，是非凸稀疏的正规化器，SCAD的拟议信号重建方法在具有高信噪比的大型系统的方法中达到了最佳性能比率（SNR）区域。
In this paper, we consider the signal detection problem of overloaded massive multi-user multi-input multi-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) and single carrier block transmission with cyclic prefix (SC-CP) systems. For the systems, we employ iterative weighted sum of complex sparse regularizers with group sparsity (IWSCSR-GS) optimization, which is a complex discrete-valued vector reconstruction method that uses discreteness of symbols to estimate unknown vectors, and propose a signal reconstruction method using piecewise continuous nonconvex sparse regularizers, such as smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP), in the optimization problem. Computer simulation results demonstrate that the proposed signal reconstruction method with MCP achieves better symbol error rate (SER) performance than that of not only IWSCSR-GS with l1 norm but also that with lp norm (p=0,1/2,2/3) or l1-l2 difference, which are nonconvex sparse regularizers, and the proposed signal reconstruction method with SCAD achieves the best performance among the methods for large systems with high signal-to-noise ratio (SNR) region.